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Supramax Bulk Carrier Market Forecasting with Technical Indicators and Neural Networks KCI 등재

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Journal of Korean Navigation and Port Reserch (한국항해항만학회지)
한국항해항만학회 (Korean Institute of Navigation and Port Research)
초록

Supramax bulk carriers cover a wide range of ocean transportation requirements, from major to minor bulk cargoes. Market forecasting for this segment has posed a challenge to researchers, due to complexity involved, on the demand side of the forecasting model. This paper addresses this issue by using technical indicators as input features, instead of complicated supply-demand variables. Artificial neural networks (ANN), one of the most popular machine-learning tools, were used to replace classical time-series models. Results revealed that ANN outperformed the benchmark binomial logistic regression model, and predicted direction of the spot market with more than 70% accuracy. Results obtained in this paper, can enable chartering desks to make better short-term chartering decisions.

목차
Abstract
 1. Introduction
 2. Data and Modelling
  2.1 Data
  2.2 Penalized Logistic Regression
  2.3 Artificial Neural Networks
 3. Empirical Results
  3.1 Artificial Neural Networks
  3.2 Penalized Logistic Regression
 4. Conclusion
 References
저자
  • Sang-Seop Lim(Division of Shipping Management, Korea Maritime and Ocean University)
  • Hee-Sung Yun(Centre for Shipping Big Data Analytics, Korea Maritime Institute) Corresponding author